library("FRESA.CAD")
library(readxl)
library(igraph)
library(umap)
library(tsne)
library(entropy)
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)
trainLabeled <- read.delim("~/GitHub/FCA/Data/trainSet.txt")
validLabeled <- read.delim("~/GitHub/FCA/Data/arcene_valid.txt")
wholeArceneSet <- rbind(trainLabeled,validLabeled)
wholeArceneSet$Labels <- 1*(wholeArceneSet$Labels > 0)
wholeArceneSet[,1:ncol(trainLabeled)] <- sapply(wholeArceneSet,as.double)
studyName <- "ARCENE"
dataframe <- wholeArceneSet
outcome <- "Labels"
thro <- 0.8
cexheat = 0.10
TopVariables <- 10
Some libraries
library(psych)
library(whitening)
library("vioplot")
library("rpart")
pander::pander(c(rows=nrow(dataframe),col=ncol(dataframe)-1))
| rows | col |
|---|---|
| 200 | 10000 |
pander::pander(table(dataframe[,outcome]))
| 0 | 1 |
|---|---|
| 112 | 88 |
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
largeSet <- length(varlist) > 1500
Scaling and removing near zero variance columns and highly co-linear(r>0.99999) columns
### Some global cleaning
sdiszero <- apply(dataframe,2,sd) > 1.0e-16
dataframe <- dataframe[,sdiszero]
varlist <- colnames(dataframe)[colnames(dataframe) != outcome]
tokeep <- c(as.character(correlated_Remove(dataframe,varlist,thr=0.99999)),outcome)
dataframe <- dataframe[,tokeep]
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
iscontinous <- sapply(apply(dataframe,2,unique),length) >= 5 ## Only variables with enough samples
dataframeScaled <- FRESAScale(dataframe,method="OrderLogit")$scaledData
numsub <- nrow(dataframe)
if (numsub > 1000) numsub <- 1000
if (!largeSet)
{
hm <- heatMaps(data=dataframeScaled[1:numsub,],
Outcome=outcome,
Scale=TRUE,
hCluster = "row",
xlab="Feature",
ylab="Sample",
srtCol=45,
srtRow=45,
cexCol=cexheat,
cexRow=cexheat
)
par(op)
}
The heat map of the data
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
#cormat <- Rfast::cora(as.matrix(dataframe[,varlist]),large=TRUE)
cormat <- cor(dataframe[,varlist],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Original Correlation",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
diag(cormat) <- 0
print(max(abs(cormat)))
}
DEdataframe <- IDeA(dataframe,verbose=TRUE,thr=thro)
#>
#> Included: 7748 , Uni p: 2.565681e-05 , Outcome-Driven Size: 0 , Base Size: 966 , Rcrit: 0.2822717
#>
#>
1 <R=1.000,thr=0.900,N= 6808>, Top: 122( 305 ).[ 1 : 122 Fa= 122 : 0.900 ]( 122 , 2729 , 0 ),<|>Tot Used: 2851 , Added: 2729 , Zero Std: 0 , Max Cor: 1.000
#>
2 <R=1.000,thr=0.900,N= 6808>, Top: 435( 53 )....=( 1 )[ 2 : 435 Fa= 556 : 0.919 ]( 435 , 3567 , 122 ),<|>Tot Used: 4904 , Added: 3567 , Zero Std: 0 , Max Cor: 1.000
#>
3 <R=1.000,thr=0.900,N= 6808>, Top: 808( 50 )........[ 1 : 808 Fa= 1357 : 0.900 ]( 805 , 3459 , 556 ),<|>Tot Used: 5945 , Added: 3459 , Zero Std: 0 , Max Cor: 1.000
#>
4 <R=1.000,thr=0.900,N= 6808>, Top: 815( 16 )........=[ 2 : 815 Fa= 2151 : 0.915 ]( 811 , 2354 , 1357 ),<|>Tot Used: 6390 , Added: 2354 , Zero Std: 0 , Max Cor: 1.000
#>
5 <R=1.000,thr=0.900,N= 6808>, Top: 512( 7 ).....[ 1 : 512 Fa= 2644 : 0.900 ]( 510 , 1233 , 2151 ),<|>Tot Used: 6641 , Added: 1233 , Zero Std: 0 , Max Cor: 0.999
#>
6 <R=0.999,thr=0.900,N= 6808>, Top: 254( 5 )..=( 1 )[ 2 : 254 Fa= 2889 : 0.919 ]( 251 , 483 , 2644 ),<|>Tot Used: 6731 , Added: 483 , Zero Std: 0 , Max Cor: 0.999
#>
7 <R=0.999,thr=0.900,N= 6808>, Top: 97( 2 )[ 1 : 97 Fa= 2967 : 0.927 ]( 91 , 138 , 2889 ),<|>Tot Used: 6757 , Added: 138 , Zero Std: 0 , Max Cor: 0.998
#>
8 <R=0.998,thr=0.900,N= 6808>, Top: 28( 1 )=[ 2 : 28 Fa= 2984 : 0.913 ]( 25 , 37 , 2967 ),<|>Tot Used: 6757 , Added: 37 , Zero Std: 0 , Max Cor: 0.989
#>
9 <R=0.989,thr=0.900,N= 6808>, Top: 8( 2 )[ 1 : 8 Fa= 2990 : 0.900 ]( 7 , 8 , 2984 ),<|>Tot Used: 6760 , Added: 8 , Zero Std: 0 , Max Cor: 0.994
#>
10 <R=0.994,thr=0.900,N= 6808>, Top: 2( 2 )[ 1 : 2 Fa= 2990 : 0.900 ]( 1 , 2 , 2990 ),<|>Tot Used: 6760 , Added: 2 , Zero Std: 0 , Max Cor: 0.952
#>
11 <R=0.952,thr=0.900,N= 6808>, Top: 1( 1 )[ 1 : 1 Fa= 2991 : 0.900 ]( 1 , 1 , 2990 ),<|>Tot Used: 6760 , Added: 1 , Zero Std: 0 , Max Cor: 0.900
#>
12 <R=0.900,thr=0.800,N= 2673>, Top: 1011( 1 ).........=( 1 )[ 2 : 1011 Fa= 3318 : 0.873 ]( 967 , 1283 , 2991 ),<|>Tot Used: 6802 , Added: 1283 , Zero Std: 0 , Max Cor: 0.997
#>
13 <R=0.997,thr=0.900,N= 292>, Top: 142( 1 ).[ 1 : 142 Fa= 3351 : 0.900 ]( 141 , 148 , 3318 ),<|>Tot Used: 6802 , Added: 148 , Zero Std: 0 , Max Cor: 0.995
#>
14 <R=0.995,thr=0.900,N= 292>, Top: 36( 1 )[ 1 : 36 Fa= 3368 : 0.900 ]( 36 , 36 , 3351 ),<|>Tot Used: 6802 , Added: 36 , Zero Std: 0 , Max Cor: 0.936
#>
15 <R=0.936,thr=0.900,N= 292>, Top: 2( 1 )[ 1 : 2 Fa= 3370 : 0.900 ]( 2 , 2 , 3368 ),<|>Tot Used: 6802 , Added: 2 , Zero Std: 0 , Max Cor: 0.900
#>
16 <R=0.900,thr=0.800,N= 750>, Top: 243( 2 )..=( 1 )[ 2 : 243 Fa= 3419 : 0.820 ]( 232 , 341 , 3370 ),<|>Tot Used: 6808 , Added: 341 , Zero Std: 0 , Max Cor: 0.984
#>
17 <R=0.984,thr=0.900,N= 62>, Top: 31( 1 )[ 1 : 31 Fa= 3429 : 0.900 ]( 31 , 31 , 3419 ),<|>Tot Used: 6808 , Added: 31 , Zero Std: 0 , Max Cor: 0.972
#>
18 <R=0.972,thr=0.900,N= 62>, Top: 5( 1 )[ 1 : 5 Fa= 3432 : 0.900 ]( 5 , 5 , 3429 ),<|>Tot Used: 6808 , Added: 5 , Zero Std: 0 , Max Cor: 0.955
#>
19 <R=0.955,thr=0.900,N= 62>, Top: 1( 1 )[ 1 : 1 Fa= 3433 : 0.900 ]( 1 , 1 , 3432 ),<|>Tot Used: 6808 , Added: 1 , Zero Std: 0 , Max Cor: 0.900
#>
20 <R=0.900,thr=0.800,N= 256>, Top: 79( 1 )[ 1 : 79 Fa= 3444 : 0.800 ]( 74 , 119 , 3433 ),<|>Tot Used: 6813 , Added: 119 , Zero Std: 0 , Max Cor: 0.986
#>
21 <R=0.986,thr=0.900,N= 22>, Top: 11( 1 )[ 1 : 11 Fa= 3446 : 0.900 ]( 11 , 11 , 3444 ),<|>Tot Used: 6813 , Added: 11 , Zero Std: 0 , Max Cor: 0.921
#>
22 <R=0.921,thr=0.900,N= 22>, Top: 1( 1 )[ 1 : 1 Fa= 3446 : 0.900 ]( 1 , 1 , 3446 ),<|>Tot Used: 6813 , Added: 1 , Zero Std: 0 , Max Cor: 0.892
#>
23 <R=0.892,thr=0.800,N= 73>, Top: 27( 1 )[ 1 : 27 Fa= 3450 : 0.800 ]( 26 , 39 , 3446 ),<|>Tot Used: 6815 , Added: 39 , Zero Std: 0 , Max Cor: 0.983
#>
24 <R=0.983,thr=0.900,N= 8>, Top: 4( 1 )[ 1 : 4 Fa= 3451 : 0.900 ]( 4 , 4 , 3450 ),<|>Tot Used: 6815 , Added: 4 , Zero Std: 0 , Max Cor: 0.905
#>
25 <R=0.905,thr=0.900,N= 8>, Top: 1( 1 )[ 1 : 1 Fa= 3452 : 0.900 ]( 1 , 1 , 3451 ),<|>Tot Used: 6815 , Added: 1 , Zero Std: 0 , Max Cor: 0.892
#>
26 <R=0.892,thr=0.800,N= 6>, Top: 3( 1 )[ 1 : 3 Fa= 3453 : 0.800 ]( 3 , 3 , 3452 ),<|>Tot Used: 6815 , Added: 3 , Zero Std: 0 , Max Cor: 0.959
#>
27 <R=0.959,thr=0.900,N= 2>, Top: 1( 1 )[ 1 : 1 Fa= 3453 : 0.900 ]( 1 , 1 , 3453 ),<|>Tot Used: 6815 , Added: 1 , Zero Std: 0 , Max Cor: 0.925
#>
28 <R=0.925,thr=0.900,N= 2>, Top: 1( 1 )[ 1 : 1 Fa= 3453 : 0.900 ]( 1 , 1 , 3453 ),<|>Tot Used: 6815 , Added: 1 , Zero Std: 0 , Max Cor: 0.867
#>
29 <R=0.867,thr=0.800,N= 2>, Top: 1( 1 )[ 1 : 1 Fa= 3453 : 0.800 ]( 1 , 1 , 3453 ),<|>Tot Used: 6815 , Added: 1 , Zero Std: 0 , Max Cor: 0.800
#>
30 <R=0.800,thr=0.800,N= 2>
#>
[ 30 ], 0.7999954 Decor Dimension: 6815 Nused: 6815 . Cor to Base: 3366 , ABase: 39 , Outcome Base: 0
#>
varlistc <- colnames(DEdataframe)[colnames(DEdataframe) != outcome]
pander::pander(sum(apply(dataframe[,varlist],2,var)))
63594442
pander::pander(sum(apply(DEdataframe[,varlistc],2,var)))
6608460
pander::pander(entropy(discretize(unlist(dataframe[,varlist]), 256)))
3.08
pander::pander(entropy(discretize(unlist(DEdataframe[,varlistc]), 256)))
1.63
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
UPLTM <- attr(DEdataframe,"UPLTM")
gplots::heatmap.2(1.0*(abs(UPLTM)>0),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Decorrelation matrix",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Beta|>0",
xlab="Output Feature", ylab="Input Feature")
par(op)
}
if (!largeSet)
{
cormat <- cor(DEdataframe[,varlistc],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Correlation after IDeA",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
par(op)
diag(cormat) <- 0
print(max(abs(cormat)))
}
if (nrow(dataframe) < 1000)
{
classes <- unique(dataframe[1:numsub,outcome])
raincolors <- rainbow(length(classes))
names(raincolors) <- classes
datasetframe.umap = umap(scale(dataframe[1:numsub,varlist]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: Original",t='n')
text(datasetframe.umap$layout,labels=dataframe[1:numsub,outcome],col=raincolors[dataframe[1:numsub,outcome]+1])
}
if (nrow(dataframe) < 1000)
{
datasetframe.umap = umap(scale(DEdataframe[1:numsub,varlistc]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: After IDeA",t='n')
text(datasetframe.umap$layout,labels=DEdataframe[1:numsub,outcome],col=raincolors[DEdataframe[1:numsub,outcome]+1])
}
univarRAW <- uniRankVar(varlist,
paste(outcome,"~1"),
outcome,
dataframe,
rankingTest="AUC")
100 : V100 200 : V201 300 : V302 400 : V402 500 : V504
600 : V606 700 : V707 800 : V807 900 : V907 1000 : V1008
1100 : V1108 1200 : V1209 1300 : V1309 1400 : V1409 1500 : V1509
1600 : V1610 1700 : V1710 1800 : V1810 1900 : V1911 2000 : V2012
2100 : V2113 2200 : V2213 2300 : V2313 2400 : V2417 2500 : V2518
2600 : V2620 2700 : V2722 2800 : V2822 2900 : V2922 3000 : V3023
3100 : V3123 3200 : V3223 3300 : V3326 3400 : V3428 3500 : V3528
3600 : V3629 3700 : V3734 3800 : V3835 3900 : V3935 4000 : V4038
4100 : V4140 4200 : V4243 4300 : V4344 4400 : V4445 4500 : V4547
4600 : V4649 4700 : V4751 4800 : V4853 4900 : V4954 5000 : V5055
5100 : V5156 5200 : V5256 5300 : V5360 5400 : V5462 5500 : V5564
5600 : V5666 5700 : V5768 5800 : V5868 5900 : V5970 6000 : V6070
6100 : V6171 6200 : V6271 6300 : V6372 6400 : V6473 6500 : V6573
6600 : V6675 6700 : V6777 6800 : V6881 6900 : V6983 7000 : V7088
7100 : V7190 7200 : V7291 7300 : V7393 7400 : V7496 7500 : V7597
7600 : V7701 7700 : V7803 7800 : V7904 7900 : V8007 8000 : V8108
8100 : V8209 8200 : V8310 8300 : V8414 8400 : V8516 8500 : V8620
8600 : V8721 8700 : V8822 8800 : V8925 8900 : V9026 9000 : V9128
9100 : V9232 9200 : V9332 9300 : V9433 9400 : V9533 9500 : V9638
9600 : V9739 9700 : V9841 9800 : V9944
univarDe <- uniRankVar(varlistc,
paste(outcome,"~1"),
outcome,
DEdataframe,
rankingTest="AUC",
)
100 : V100 200 : La_V201 300 : V302 400 : La_V402 500 : V504
600 : V606 700 : V707 800 : V807 900 : La_V907 1000 : La_V1008
1100 : La_V1108 1200 : V1209 1300 : La_V1309 1400 : La_V1409 1500 :
La_V1509
1600 : La_V1610 1700 : La_V1710 1800 : La_V1810 1900 : La_V1911 2000 :
La_V2012
2100 : La_V2113 2200 : La_V2213 2300 : V2313 2400 : La_V2417 2500 :
La_V2518
2600 : V2620 2700 : V2722 2800 : V2822 2900 : V2922 3000 :
La_V3023
3100 : La_V3123 3200 : La_V3223 3300 : V3326 3400 : V3428 3500 :
La_V3528
3600 : La_V3629 3700 : La_V3734 3800 : La_V3835 3900 : La_V3935 4000 :
La_V4038
4100 : La_V4140 4200 : La_V4243 4300 : V4344 4400 : V4445 4500 :
La_V4547
4600 : La_V4649 4700 : La_V4751 4800 : V4853 4900 : La_V4954 5000 :
La_V5055
5100 : V5156 5200 : La_V5256 5300 : V5360 5400 : La_V5462 5500 :
La_V5564
5600 : La_V5666 5700 : La_V5768 5800 : La_V5868 5900 : La_V5970 6000 :
La_V6070
6100 : La_V6171 6200 : La_V6271 6300 : La_V6372 6400 : La_V6473 6500 :
V6573
6600 : La_V6675 6700 : La_V6777 6800 : V6881 6900 : La_V6983 7000 :
La_V7088
7100 : La_V7190 7200 : V7291 7300 : La_V7393 7400 : La_V7496 7500 :
La_V7597
7600 : La_V7701 7700 : La_V7803 7800 : La_V7904 7900 : V8007 8000 :
La_V8108
8100 : La_V8209 8200 : La_V8310 8300 : La_V8414 8400 : La_V8516 8500 :
V8620
8600 : La_V8721 8700 : La_V8822 8800 : La_V8925 8900 : La_V9026 9000 :
V9128
9100 : La_V9232 9200 : La_V9332 9300 : La_V9433 9400 : V9533 9500 :
La_V9638
9600 : La_V9739 9700 : La_V9841 9800 : V9944
univariate_columns <- c("caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC")
##top variables
topvar <- c(1:length(varlist)) <= TopVariables
tableRaw <- univarRAW$orderframe[topvar,univariate_columns]
pander::pander(tableRaw)
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| V5005 | 314.7 | 72.9 | 239 | 83.6 | 0.18466 | 0.772 |
| V4960 | 47.5 | 49.3 | 124 | 97.3 | 0.19534 | 0.751 |
| V2309 | 43.1 | 45.3 | 113 | 89.0 | 0.18665 | 0.751 |
| V8368 | 44.9 | 46.1 | 116 | 90.6 | 0.21091 | 0.751 |
| V312 | 47.2 | 48.0 | 122 | 94.7 | 0.21678 | 0.750 |
| V3365 | 46.3 | 46.9 | 119 | 92.5 | 0.22139 | 0.749 |
| V9617 | 40.9 | 44.7 | 109 | 87.5 | 0.15591 | 0.749 |
| V414 | 47.5 | 50.4 | 125 | 100.4 | 0.16265 | 0.749 |
| V9092 | 33.9 | 63.1 | 124 | 132.6 | 0.00199 | 0.748 |
| V1936 | 316.0 | 79.5 | 243 | 79.2 | 0.28495 | 0.748 |
topLAvar <- univarDe$orderframe$Name[str_detect(univarDe$orderframe$Name,"La_")]
topLAvar <- unique(c(univarDe$orderframe$Name[topvar],topLAvar[1:as.integer(TopVariables/2)]))
finalTable <- univarDe$orderframe[topLAvar,univariate_columns]
pander::pander(finalTable)
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| La_V5357 | 11.737 | 11.44 | -0.913 | 9.88 | 1.82e-03 | 0.792 |
| La_V6838 | 11.780 | 6.29 | 2.208 | 11.57 | 3.11e-07 | 0.778 |
| La_V2185 | 19.065 | 31.48 | -6.343 | 25.57 | 1.54e-04 | 0.777 |
| La_V571 | -11.330 | 3.74 | -7.865 | 3.90 | 1.51e-01 | 0.772 |
| La_V2747 | 37.862 | 27.41 | 13.202 | 23.24 | 6.29e-03 | 0.766 |
| La_V5931 | -22.157 | 40.35 | 22.116 | 46.02 | 1.06e-04 | 0.764 |
| La_V316 | 0.699 | 1.40 | -0.609 | 1.64 | 5.75e-03 | 0.764 |
| La_V298 | -12.286 | 29.55 | 12.450 | 27.56 | 2.34e-04 | 0.748 |
| La_V9281 | 2.079 | 3.97 | -4.438 | 10.19 | 5.92e-09 | 0.747 |
| V5801 | 503.182 | 107.04 | 385.036 | 144.30 | 1.40e-01 | 0.744 |
dc <- getLatentCoefficients(DEdataframe)
fscores <- attr(DEdataframe,"fscore")
pander::pander(c(mean=mean(sapply(dc,length)),total=length(dc),fraction=length(dc)/(ncol(dataframe)-1)))
| mean | total | fraction |
|---|---|---|
| 3.5 | 6562 | 0.666 |
theCharformulas <- attr(dc,"LatentCharFormulas")
finalTable <- rbind(finalTable,tableRaw[topvar[!(topvar %in% topLAvar)],univariate_columns])
orgnamez <- rownames(finalTable)
orgnamez <- str_remove_all(orgnamez,"La_")
finalTable$RAWAUC <- univarRAW$orderframe[orgnamez,"ROCAUC"]
finalTable$DecorFormula <- theCharformulas[rownames(finalTable)]
finalTable$fscores <- fscores[rownames(finalTable)]
Final_Columns <- c("DecorFormula","caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC","RAWAUC","fscores")
finalTable <- finalTable[order(-finalTable$ROCAUC),]
pander::pander(finalTable[,Final_Columns])
| DecorFormula | caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | RAWAUC | fscores | |
|---|---|---|---|---|---|---|---|---|---|
| La_V5357 | - (1.068)V3417 + V5357 - (0.034)V9171 | 11.737 | 11.44 | -0.913 | 9.88 | 1.82e-03 | 0.792 | 0.603 | -1 |
| La_V6838 | + (0.013)V1474 - (1.016)V5947 + V6838 | 11.780 | 6.29 | 2.208 | 11.57 | 3.11e-07 | 0.778 | 0.670 | 0 |
| La_V2185 | - (0.723)V1380 + V2185 | 19.065 | 31.48 | -6.343 | 25.57 | 1.54e-04 | 0.777 | 0.592 | 0 |
| La_V571 | - (0.829)V405 + V571 - (0.203)V7374 | -11.330 | 3.74 | -7.865 | 3.90 | 1.51e-01 | 0.772 | 0.598 | -1 |
| V5005 | NA | 314.739 | 72.85 | 239.304 | 83.62 | 1.85e-01 | 0.772 | 0.772 | NA |
| La_V2747 | + V2747 - (0.906)V3001 | 37.862 | 27.41 | 13.202 | 23.24 | 6.29e-03 | 0.766 | 0.624 | 2 |
| La_V5931 | - (0.662)V4487 + V5931 | -22.157 | 40.35 | 22.116 | 46.02 | 1.06e-04 | 0.764 | 0.517 | 13 |
| La_V316 | + V316 - (0.355)V1380 + (1.902)V6976 - (2.551)V8743 | 0.699 | 1.40 | -0.609 | 1.64 | 5.75e-03 | 0.764 | 0.545 | -3 |
| V4960 | NA | 47.511 | 49.25 | 123.696 | 97.33 | 1.95e-01 | 0.751 | 0.751 | NA |
| V2309 | NA | 43.057 | 45.32 | 112.616 | 89.00 | 1.87e-01 | 0.751 | 0.751 | NA |
| V8368 | NA | 44.886 | 46.06 | 116.036 | 90.62 | 2.11e-01 | 0.751 | 0.751 | NA |
| V312 | NA | 47.182 | 48.01 | 121.634 | 94.73 | 2.17e-01 | 0.750 | 0.750 | NA |
| V3365 | NA | 46.261 | 46.93 | 119.054 | 92.52 | 2.21e-01 | 0.749 | 0.749 | NA |
| V9617 | NA | 40.886 | 44.75 | 108.848 | 87.49 | 1.56e-01 | 0.749 | 0.749 | NA |
| V414 | NA | 47.466 | 50.45 | 125.348 | 100.36 | 1.63e-01 | 0.749 | 0.749 | NA |
| V9092 | NA | 33.886 | 63.11 | 123.607 | 132.62 | 1.99e-03 | 0.748 | 0.748 | NA |
| La_V298 | + V298 - (0.713)V4487 | -12.286 | 29.55 | 12.450 | 27.56 | 2.34e-04 | 0.748 | 0.549 | 1 |
| V1936 | NA | 315.955 | 79.48 | 243.062 | 79.21 | 2.85e-01 | 0.748 | 0.748 | NA |
| La_V9281 | - (0.026)V403 - (1.043)V1070 + V9281 | 2.079 | 3.97 | -4.438 | 10.19 | 5.92e-09 | 0.747 | 0.608 | 1 |
| V5801 | NA | 503.182 | 107.04 | 385.036 | 144.30 | 1.40e-01 | 0.744 | 0.744 | 10 |
featuresnames <- colnames(dataframe)[colnames(dataframe) != outcome]
pc <- prcomp(dataframe[,iscontinous],center = TRUE,scale. = TRUE) #principal components
predPCA <- predict(pc,dataframe[,iscontinous])
PCAdataframe <- as.data.frame(cbind(predPCA,dataframe[,!iscontinous]))
colnames(PCAdataframe) <- c(colnames(predPCA),colnames(dataframe)[!iscontinous])
#plot(PCAdataframe[,colnames(PCAdataframe)!=outcome],col=dataframe[,outcome],cex=0.65,cex.lab=0.5,cex.axis=0.75,cex.sub=0.5,cex.main=0.75)
#pander::pander(pc$rotation)
PCACor <- cor(PCAdataframe[,colnames(PCAdataframe) != outcome])
gplots::heatmap.2(abs(PCACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "PCA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
EFAdataframe <- dataframeScaled
if (length(iscontinous) < 2000)
{
topred <- min(length(iscontinous),nrow(dataframeScaled),ncol(predPCA)/2)
if (topred < 2) topred <- 2
uls <- fa(dataframeScaled[,iscontinous],nfactors=topred,rotate="varimax",warnings=FALSE) # EFA analysis
predEFA <- predict(uls,dataframeScaled[,iscontinous])
EFAdataframe <- as.data.frame(cbind(predEFA,dataframeScaled[,!iscontinous]))
colnames(EFAdataframe) <- c(colnames(predEFA),colnames(dataframeScaled)[!iscontinous])
EFACor <- cor(EFAdataframe[,colnames(EFAdataframe) != outcome])
gplots::heatmap.2(abs(EFACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "EFA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
}
par(op)
par(xpd = TRUE)
dataframe[,outcome] <- factor(dataframe[,outcome])
rawmodel <- rpart(paste(outcome,"~."),dataframe,control=rpart.control(maxdepth=3))
pr <- predict(rawmodel,dataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(rawmodel,main="Raw",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(rawmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,dataframe[,outcome]==0))
}
pander::pander(table(dataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 109 | 3 |
| 1 | 13 | 75 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.920 | 0.873 | 0.954 |
| 3 | se | 0.852 | 0.761 | 0.919 |
| 4 | sp | 0.973 | 0.924 | 0.994 |
| 6 | diag.or | 209.615 | 57.738 | 761.000 |
par(op)
par(xpd = TRUE)
DEdataframe[,outcome] <- factor(DEdataframe[,outcome])
IDeAmodel <- rpart(paste(outcome,"~."),DEdataframe,control=rpart.control(maxdepth=3))
pr <- predict(IDeAmodel,DEdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(IDeAmodel,main="IDeA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(IDeAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,DEdataframe[,outcome]==0))
}
pander::pander(table(DEdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 107 | 5 |
| 1 | 10 | 78 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.925 | 0.879 | 0.957 |
| 3 | se | 0.886 | 0.801 | 0.944 |
| 4 | sp | 0.955 | 0.899 | 0.985 |
| 6 | diag.or | 166.920 | 54.874 | 507.747 |
par(op)
par(xpd = TRUE)
PCAdataframe[,outcome] <- factor(PCAdataframe[,outcome])
PCAmodel <- rpart(paste(outcome,"~."),PCAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(PCAmodel,PCAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(PCAmodel,main="PCA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(PCAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,PCAdataframe[,outcome]==0))
}
pander::pander(table(PCAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 104 | 8 |
| 1 | 39 | 49 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.765 | 0.700 | 0.822 |
| 3 | se | 0.557 | 0.447 | 0.663 |
| 4 | sp | 0.929 | 0.864 | 0.969 |
| 6 | diag.or | 16.333 | 7.100 | 37.573 |
par(op)
EFAdataframe[,outcome] <- factor(EFAdataframe[,outcome])
EFAmodel <- rpart(paste(outcome,"~."),EFAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(EFAmodel,EFAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(EFAmodel,main="EFA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(EFAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,EFAdataframe[,outcome]==0))
}
pander::pander(table(EFAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 109 | 3 |
| 1 | 13 | 75 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.920 | 0.873 | 0.954 |
| 3 | se | 0.852 | 0.761 | 0.919 |
| 4 | sp | 0.973 | 0.924 | 0.994 |
| 6 | diag.or | 209.615 | 57.738 | 761.000 |
par(op)